‫Dynamic World هي مجموعة بيانات لاستخدام الأراضي/الغطاء الأرضي (LULC) بدقة 10 أمتار في الوقت الفعلي تقريبًا (NRT)، وتشمل احتمالات الفئات ومعلومات التصنيف لتسع فئات. تتوفّر توقّعات Dynamic World لمجموعة Sentinel-2 L1C من 27-06-2015 حتى الآن. تتراوح فترة إعادة زيارة القمر الصناعي Sentinel-2 بين يومين و5 أيام حسب خط العرض. Dynamic World …

This data is long-term lake dynamic (surface elevation, area, and volume) derived from topographic datasets and Landsat imagery in Google Earth Engine.

The Dynamic World Training Data is a dataset of over 5 billion pixels of human-labeled ESA Sentinel-2 satellite image, distributed over 24000 tiles collected from all over the world. The dataset is designed to train and validate automated land use and land cover mapping algorithms. The 10m resolution 5.1km-by-5.1km tiles are densely labeled using a ten category classification schema indicating general land use land cover categories. The dataset was created between 2019-08-01 and 2020-02-28, using satellite imagery observations from 2019, with approximately 10% of observations extending back to 2017 in very cloudy regions of the world. This dataset is a component of the National Geographic Society - Google - World Resources Institute Dynamic World project. […]

Using 335,709 Landsat images on the Google Earth Engine, we built the first Landsat-derived annual land cover product of China (CLCD) from 1985 to 2019. We collected the training samples by combining stable samples extracted from China's Land-Use/Cover Datasets (CLUD), and visually-interpreted samples from satellite time-series data, Google Earth and Google Map. Several temporal metrics were constructed via all available Landsat data and fed to the random forest classifier to obtain classification results. A post-processing method incorporating spatial-temporal filtering and logical reasoning was further proposed to improve the spatial-temporal consistency of CLCD.

"*_albert.tif" are projected files via a proj4 string "+proj=aea +lat_1=25 +lat_2=47 +lat_0=0 +lon_0=105 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs".

CLCD in 2023 is now available.

1. Given that the USGS no longer maintains the Landsat Collection 1 data, we are now using the Collection 2 SR data to update the CLCD.

2. All files in this version have been exported as Cloud Optimized GeoTIFF for more efficient processing on the cloud. Please check here for more details.

3. Internal overviews and color tables are built into each file to speed up software loading and rendering.

The Terra and Aqua combined Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Dynamics (MCD12Q2) Version 6.1 data product provides global land surface phenology metrics at yearly intervals. The MCD12Q2 Version 6.1 data product is derived from time series of the 2-band Enhanced Vegetation Index (EVI2) calculated from MODIS Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance(NBAR). Vegetation phenology metrics at 500 meter spatial resolution are identified for up to two detected growing cycles per year. For pixels with more than two valid vegetation cycles, the data represent the two cycles with the largest NBAR-EVI2 amplitudes. Each asset contains bands are layers for the total number of vegetation cycles detected for the product year, the onset of greenness, greenup midpoint, maturity, peak greenness, senescence, greendown midpoint, dormancy, EVI2 minimum, EVI2 amplitude, integrated EVI2 over a vegetation cycle, as well as overall and phenology metric-specific quality information. For areas where the NBAR-EVI2 values are missing due to cloud cover or other reasons, the data gaps are filled with good quality NBAR-EVI2 values from the year directly preceding or following the product year.

This dataset includes three parts:(1) The mapped global annual urban dynamics (GAUD) and green recovery from 1985 to 2015 at a 30-m resolution. This part of data is organized by 10-degree grids (totally 224).Shapefiles of 10-degree grids can be found in "grids_world.zip".Urban expansion data is packaged in "urban_grid_i.zip" (i ranges from 0 to 223). Green recovery data is packaged in "green_grid_0-223.zip".Their format is GeoTiff, and for each pixel, values from 1985 to 2015 demonstrate the urbanized or green recovery year, while 0 means no data.(2) The interpreted samples of urban extent in 1985 and 2015, and urbanized year during 1985 and 2015. This part of data is for examining the accuracies of our data fusion and temporal segmentation approach. Interpreted urban extent is packaged in "Ref_tif_clip_1985.rar" and "Ref_tif_clip_2015.rar".Its format is GeoTiff, and for each pixel, value 1 means urban areas, while 0 means non-urban areas.Valid samples of urbanized year can be found in "validation_urbanized_year.xls".(3) A demo of NUACI calculation and urbanized years dectection can be found at link:https://code.earthengine.google.com/1c901129fa8c9d81b292824e8fb4ff1c22.05.2021:Solved the problem of stripe after image mosaic (grid 138)

Land cover is the physical evidence on the surface of the Earth. As the cause and result of global environmental change, land cover change (LCC) influences the global energy balance and biogeochemical cycles. Continuous and dynamic monitoring of global LC is urgently needed. Effective monitoring and comprehensive analysis of LCC at the global scale are rare. With the latest version of GLASS (The Global Land Surface Satellite) CDRs (Climate Data Records) from 1982 to 2015, we built the first record of 34-year long annual dynamics of global land cover (GLASS-GLC) at 5 km resolution using the Google Earth Engine (GEE) platform. Compared to earlier global LC products, GLASS-GLC is characterized by high consistency, more detailed, and longer temporal coverage. The average overall accuracy for the 34 years each with 7 classes, including cropland, forest, grassland, shrubland, tundra, barren land, and snow/ice, is 82.81 % based on 2431 test sample units. We implemented a systematic uncertainty analysis and carried out a comprehensive spatiotemporal pattern analysis. Significant changes at various scales were found, including barren land loss and cropland gain in the tropics, forest gain in northern hemisphere and grassland loss in Asia, etc. A global quantitative analysis of human factors showed that the average human impact level in areas with significant LCC was about 25.49 %. The anthropogenic influence has a strong correlation with the noticeable vegetation gain, especially for forest. Based on GLASS-GLC, we can conduct long-term LCC analysis, improve our understanding of global environmental change, and mitigate its negative impact. GLASS-GLC will be further applied in Earth system modeling to facilitate research on global carbon and water cycling, vegetation dynamics, and climate change. This GLASS-GLC data set is related to the paper at doi:10.5194/essd-2019-23. It consists of one readme file and 34 GeoTIFF files of annual 5 km global maps from 1982 to 2015 in a WGS 84 projection.

This data release comprises the raster data files and code necessary to perform all analyses presented in the associated publication. The 16 TIF raster data files are classified surface water maps created using the Dynamic Surface Water Extent (DSWE) model implemented in Google Earth Engine using published technical documents. The 16 tiles cover the country of Cambodia, a flood-prone country in Southeast Asia lacking a comprehensive stream gauging network. Each file includes 372 bands. Bands represent surface water for each month from 1988 to 2018, and are stacked from oldest (Band 1 - January 1988) to newest (Band 372 - December 2018). DSWE classifies pixels unobscured by cloud, cloud shadow, or snow into five categories of ground surface inundation; in addition to not-water (class 0) and water (class 1), the DSWE algorithm distinguishes pixels that are less distinctly inundated (class 2: “moderate confidence”), comprise a mixture of vegetation and water (class 3: “potential wetland”), or are of marginal validity (class 4: “water or wetland - low confidence”). Class 9 is applied to classify clouds, shadows and hill shade. Two additional documents accompany the raster image files and XML metadata. The first provides a key representing the general location of each raster file. The second file includes all Google Earth Engine Javascript code, which can be used online (https://code.earthengine.google.com/) to replicate the monthly DSWE map time series for Cambodia, or for any other location on Earth. The code block includes comments to explain how each step works. These data support the following publication: These data support the following publication: Soulard, C.E., Walker, J.J., and Petrakis, R.E., 2020, Implementation of a Surface Water Extent Model in Cambodia using Cloud-Based Remote Sensing: Remote Sensing, v. 12, no. 6, p. 984, https://doi.org/10.3390/rs12060984.

The objectives of this study are to map the past and present LULC accurately, ensure the precision of classification and prediction models, and estimate future LULC changes using Google Earth Engine. LULC classification from 2014-2024 was performed using robust SmileCART and future LULC prediction for 2024, 2030, 2040, 2050, 2060 and 2070 was conducted using the Smile Random Forest (RF) algorithm incorporating various socio-economic variables. The classification model SmileCART was trained by using the European Space Agency World Cover data and validated through Landsat 8 satellite imagery, achieving training and test accuracies of 83% and 84% respectively. SmileRF prediction model showed an accuracy of 87% with a kappa coefficient of 0.86. The results indicate a decline in vegetation cover, snow & ice and water bodies, and an increase in built-up areas and cropland, with other classes showing fluctuations in the Jhelum and Chenab River basins from 2014 to 2070. These insights contribute to a deeper understanding of these critical watersheds, informing sustainable land management, water resource planning, and decision-making for the future sustainable development of the Jhelum and Chenab River basins using GEE and remote sensing data.

The dataset comprises a Landsat-derived assessment of monthly surface water extent within the study area (California's greater Central Valley). The surface water dataset is based on the algorithm for the Dynamic Surface Water Extent (DSWE) (Jones, 2019), which was adapted to the Google Earth Engine JavaScript environment. The level of spatial aggregation is by level-8 hydrologic unit code (HUC).

Here we developed annual irrigated cropland maps across China (IrriMap_CN) at 500-m resolution from 2000 to 2019, using MODIS data, machine-learning method, and Google Earth Engine platform. The spatial reference system of this dataset is EPSG: 4326 (WGS-1984). Readers can refer to the following publications for more details on the methods. Zhang, C., Dong, J., Ge, Q., 2022. IrriMap_CN: Annual irrigation maps across China in 2000–2019 based on satellite observations, environmental variables, and machine learning. Remote Sens. Environ. https://dx.doi.org/10.1016/j.rse.2022.113184 Zhang, C., Dong, J., Xie, Y., Zhang, X., Ge, Q., 2022. Mapping irrigated croplands in China using a synergetic training sample generating method, machine learning classifier, and Google Earth Engine. Int. J. Appl. Earth Obs. Geoinf. 112, 102888. https://dx.doi.org/10.1016/j.jag.2022.102888

In addition, we also posted the link of IrriMap_Syn dataset (The 500-m irrigated cropland maps in China based on a synergy mapping method) and relevant publications as follows. The IrriMap_Syn dataset, as statistics-constraint irrigation maps, provide important ground truth data (training samples) for the generation of IrriMap_CN. Zhang, C., Dong, J., Ge, Q., 2022. Mapping 20 years of irrigated croplands in China using MODIS and statistics and existing irrigation products. Sci. Data 9, 407. https://dx.doi.org/10.1038/s41597-022-01522-z Zhang, C., Dong, J., Ge, Q., 2022. The 500-m irrigated cropland maps in China during 2000-2019 based on a synergy mapping method. figshare http://doi.org/10.6084/m9.figshare.19352501 Zhang, C., Dong, J., Zuo, L., Ge, Q., 2022. Tracking spatiotemporal dynamics of irrigated croplands in China from 2000 to 2019 through the synergy of remote sensing, statistics, and historical irrigation datasets. Agric. Water Manage. 263, 107458-107470. https://dx.doi.org/10.1016/j.agwat.2022.107458

Landsat and Sentinel-2 acquisitions are among the most frequently used medium-resolution (i.e., 10-30 m) optical data. The data are extensively used in terrestrial vegetation applications, including but not limited to, land cover and land use mapping, vegetation condition and phenology monitoring, and disturbance and change mapping. While the Landsat archives alone provide over 40 years, and counting, of continuous and consistent observations, since mid-2015 Sentinel-2 has enabled a revisit frequency of up to 2-days. Although the spatio-temporal availability of both data archives is well-known at the scene level, information on the actual availability of usable (i.e., cloud-, snow-, and shade-free) observations at the pixel level needs to be explored for each study to ensure correct parametrization of used algorithms, thus robustness of subsequent analyses. However, a priori data exploration is time and resource‑consuming, thus is rarely performed. As a result, the spatio-temporal heterogeneity of usable data is often inadequately accounted for in the analysis design, risking ill-advised selection of algorithms and hypotheses, and thus inferior quality of final results. Here we present a global dataset comprising precomputed daily availability of usable Landsat and Sentinel-2 data sampled at a pixel-level in a regular 0.18°-point grid. We based the dataset on the complete 1982-2024 Landsat surface reflectance data (Collection 2) and 2015-2024 Seninel-2 top-of-the-atmosphere reflectance scenes (pre‑Collection-1 and Collection-1). Derivation of cloud-, snow-, and shade-free observations followed the methodology developed in our recent study on data availability over Europe (Lewińska et al., 2023; https://doi.org/10.20944/preprints202308.2174.v2). Furthermore, we expanded the dataset with growing season information derived based on the 2001‑2019 time series of the yearly 500 m MODIS land cover dynamics product (MCD12Q2; Collection 6). As such, our dataset presents a unique overview of the spatio-temporal availability of usable daily Landsat and Sentinel-2 data at the global scale, hence offering much-needed a priori information aiding the identification of appropriate methods and challenges for terrestrial vegetation analyses at the local to global scales. The dataset can be viewed using the dedicated GEE App (link in Related Works). As of February 2025 the dataset has been extended with the 2024 data. Methods We based our analyses on freely and openly accessible Landsat and Sentinel-2 data archives available in Google Earth Engine (Gorelick et al., 2017). We used all Landsat surface reflectance Level 2, Tier 1, Collection 2 scenes acquired with the Thematic Mapper (TM) (Earth Resources Observation And Science (EROS) Center, 1982), Enhanced Thematic Mapper (ETM+) (Earth Resources Observation And Science (EROS) Center, 1999), and Operational Land Imager (OLI) (Earth Resources Observation And Science (EROS) Center, 2013) scanners between 22nd August 1982 and 31st December 2024, and Sentinel-2 TOA reflectance Level-1C scenes (pre‑Collection-1 (European Space Agency, 2015, 2021) and Collection-1 (European Space Agency, 2022)) acquired with the MultiSpectral Instrument (MSI) between 23rd June 2015 and 31st December 2024. We implemented a conservative pixel-quality screening to identify cloud-, snow-, and shade-free land pixels. For the Landsat time series, we relied on the inherent pixel quality bands (Foga et al., 2017; Zhu & Woodcock, 2012) excluding all pixels flagged as cloud, snow, or shadow as well as pixels with the fill-in value of 20,000 (scale factor 0.0001; (Zhang et al., 2022)). Furthermore, due to the Landsat 7 orbit drift (Qiu et al., 2021) we excluded all ETM+ scenes acquired after 31st December 2020. Because Sentinel-2 Level-2A quality masks lack the desired scope and accuracy (Baetens et al., 2019; Coluzzi et al., 2018), we resorted to Level-1C scenes accompanied by the supporting Cloud Probability product. Furthermore, we employed a selection of conditions, including a threshold on Band 10 (SWIR-Cirrus), which is not available at Level‑2A. Overall, our Sentinel-2-specific cloud, shadow, and snow screening comprised:

exclusion of all pixels flagged as clouds and cirrus in the inherent ‘QA60’ cloud mask band; exclusion of all pixels with cloud probability >50% as defined in the corresponding Cloud Probability product available for each scene; exclusion of cirrus clouds (B10 reflectance >0.01); exclusion of clouds based on Cloud Displacement Analysis (CDI<‑0.5) (Frantz et al., 2018); exclusion of dark pixels (B8 reflectance <0.16) within cloud shadows modelled for each scene with scene‑specific sun parameters for the clouds identified in the previous steps. Here we assumed a cloud height of 2,000 m. exclusion of pixels within a 40-m buffer (two pixels at 20-m resolution) around each identified cloud and cloud shadow object. exclusion of snow pixels identified with a snow mask branch of the Sen2Cor processor (Main-Knorn et al., 2017).

Through applying the data screening, we generated a collection of daily availability records for Landsat and Sentinel-2 data archives. We next subsampled the resulting binary time series with a regular 0.18° x 0.18°‑point grid defined in the EPSG:4326 projection, obtaining 475,150 points located over land between ‑179.8867°W and 179.5733°E and 83.50834°N and ‑59.05167°S. Owing to the substantial amount of data comprised in the Landsat and Sentinel-2 archives and the computationally demanding process of cloud-, snow-, and shade-screening, we performed the subsampling in batches corresponding to a 4° x 4° regular grid and consolidated the final data in post-processing. We derived the pixel-specific growing season information from the 2001-2019 time series of the yearly 500‑m MODIS land cover dynamics product (MCD12Q2; Collection 6) available in Google Earth Engine. We only used information on the start and the end of a growing season, excluding all pixels with quality below ‘best’. When a pixel went through more than one growing cycle per year, we approximated a growing season as the period between the beginning of the first growing cycle and the end of the last growing cycle. To fill in data gaps arising from low-quality data and insufficiently pronounced seasonality (Friedl et al., 2019), we used a 5x5 mean moving window filter to ensure better spatial continuity of our growing season datasets. Following (Lewińska et al., 2023), we defined the start of the season as the pixel-specific 25th percentile of the 2001-2019 distribution for the start of the season dates, and the end of the season as the pixel-specific 75th percentile of the 2001-2019 distribution for end of the season dates. Finally, we subsampled the start and end of the season datasets with the same regular 0.18° x 0.18°-point grid defined in the EPSG:4326 projection. References:

Baetens, L., Desjardins, C., & Hagolle, O. (2019). Validation of Copernicus Sentinel-2 Cloud Masks Obtained from MAJA, Sen2Cor, and FMask Processors Using Reference Cloud Masks Generated with a Supervised Active Learning Procedure. Remote Sensing, 11(4), 433. https://doi.org/10.3390/rs11040433 Coluzzi, R., Imbrenda, V., Lanfredi, M., & Simoniello, T. (2018). A first assessment of the Sentinel-2 Level 1-C cloud mask product to support informed surface analyses. Remote Sensing of Environment, 217, 426–443. https://doi.org/10.1016/j.rse.2018.08.009 Earth Resources Observation And Science (EROS) Center. (1982). Collection-2 Landsat 4-5 Thematic Mapper (TM) Level-1 Data Products [Other]. U.S. Geological Survey. https://doi.org/10.5066/P918ROHC Earth Resources Observation And Science (EROS) Center. (1999). Collection-2 Landsat 7 Enhanced Thematic Mapper Plus (ETM+) Level-1 Data Products [dataset]. U.S. Geological Survey. https://doi.org/10.5066/P9TU80IG Earth Resources Observation And Science (EROS) Center. (2013). Collection-2 Landsat 8-9 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) Level-1 Data Products [Other]. U.S. Geological Survey. https://doi.org/10.5066/P975CC9B European Space Agency. (2015). Sentinel-2 MSI Level-1C TOA Reflectance [dataset]. European Space Agency. https://doi.org/10.5270/S2_-d8we2fl European Space Agency. (2021). Sentinel-2 MSI Level-1C TOA Reflectance, Collection 0 [dataset]. European Space Agency. https://doi.org/10.5270/S2_-d8we2fl European Space Agency. (2022). Sentinel-2 MSI Level-1C TOA Reflectance [dataset]. European Space Agency. https://doi.org/10.5270/S2_-742ikth Foga, S., Scaramuzza, P. L., Guo, S., Zhu, Z., Dilley, R. D., Beckmann, T., Schmidt, G. L., Dwyer, J. L., Joseph Hughes, M., & Laue, B. (2017). Cloud detection algorithm comparison and validation for operational Landsat data products. Remote Sensing of Environment, 194, 379–390. https://doi.org/10.1016/j.rse.2017.03.026 Frantz, D., Haß, E., Uhl, A., Stoffels, J., & Hill, J. (2018). Improvement of the Fmask algorithm for Sentinel-2 images: Separating clouds from bright surfaces based on parallax effects. Remote Sensing of Environment, 215, 471–481. https://doi.org/10.1016/j.rse.2018.04.046 Friedl, M., Josh, G., & Sulla-Menashe, D. (2019). MCD12Q2 MODIS/Terra+Aqua Land Cover Dynamics Yearly L3 Global 500m SIN Grid V006 [dataset]. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MCD12Q2.006 Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. https://doi.org/10.1016/j.rse.2017.06.031Lewińska K.E., Ernst S., Frantz D., Leser U., Hostert P., Global Overview of Usable Landsat and Sentinel-2 Data for 1982–2023. Data in Brief 57, (2024) https://doi.org/10.1016/j.dib.2024.111054 Main-Knorn, M., Pflug, B., Louis, J., Debaecker, V., Müller-Wilm, U., & Gascon, F. (2017). Sen2Cor for Sentinel-2. In L. Bruzzone, F. Bovolo,

Detailed Data Dictionary: https://docs.google.com/spreadsheets/d/1lM1sGcDnaH3Yl8kQOsozotvhtKMwVW4VHTjrWL1ysTs/edit?gid=889227542gid=889227542

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A novel and accurate global 30 m impervious surface dynamic dataset (GISD30) for 1985 to 2020 was produced using the spectral generalization method and time-series Landsat imagery, on the Google Earth Engine cloud-computing platform.

Correlation degree between the ecological security index and the areas of various land cover types.

This dataset contains Global Ecosystem Dynamics Investigation (GEDI) Level 4A (L4A) Version 2 predictions of the aboveground biomass density (AGBD; in Mg/ha) and estimates of the prediction standard error within each sampled geolocated laser footprint. In this version, the granules are in sub-orbits. Height metrics from simulated waveforms associated with field estimates of AGBD from multiple regions and plant functional types (PFTs) were compiled to generate a calibration dataset for models representing the combinations of world regions and PFTs (i.e., deciduous broadleaf trees, evergreen broadleaf trees, evergreen needleleaf trees, deciduous needleleaf trees, and the combination of grasslands, shrubs, and woodlands).The algorithm setting group selection used for GEDI02_A Version 2 has been modified for evergreen broadleaf trees in South America to reduce false positive errors resulting from the selection of waveform modes above ground elevation as the lowest mode. Please see User Guide for more information. The Global Ecosystem Dynamics Investigation GEDI mission aims to characterize ecosystem structure and dynamics to enable radically improved quantification and understanding of the Earth's carbon cycle and biodiversity. The GEDI instrument, attached to the International Space Station (ISS), collects data globally between 51.6° N and 51.6° S latitudes at the highest resolution and densest sampling of the 3-dimensional structure of the Earth. The GEDI instrument consists of three lasers producing a total of eight beam ground transects, which instantaneously sample eight ~25 m footprints spaced approximately every 60 m along-track. ProductDescriptionL2A VectorLARSE/GEDI/GEDI02_A_002L2A Monthly rasterLARSE/GEDI/GEDI02_A_002_MONTHLYL2A table indexLARSE/GEDI/GEDI02_A_002_INDEXL2B VectorLARSE/GEDI/GEDI02_B_002L2B Monthly rasterLARSE/GEDI/GEDI02_B_002_MONTHLYL2B table indexLARSE/GEDI/GEDI02_B_002_INDEXL4A Biomass VectorLARSE/GEDI/GEDI04_A_002L4A Monthly rasterLARSE/GEDI/GEDI04_A_002_MONTHLYL4A table indexLARSE/GEDI/GEDI04_A_002_INDEXL4B BiomassLARSE/GEDI/GEDI04_B_002

Terrascope is a Google Earth implementation of the LANDSAT MSS, TM and ETM+ ortho mosaics for the 1970s, circa 1990 and circa 2000. It is designed to allow rapid comparison of LANDSAT imagery between these periods for any view in Google Earth. The imagery is provided as superoverlays in which resolution (level of detail) increase as you zoom in on an area. Full resolution is 57 metres/pixel for the 1970's (MSS), 28.5 metres/pixel for the circa 1990 (TM) and 14.25 metres/pixel for circa 2000 (ETM+). The rationale for terrascope is to provide an easy mechanism for visual change detection for non remote-sensing specialists who may be interested in environmental change research (desertification, land use change, urbanisation, coastal fluvial and water body change), awareness raising (of hotspots for habitat loss for example), and conservation monitoring or prioritisation uses.

Terrascope brings some history to Google Earth. Google Earth imagery is largely made up of the NASA Circa 2000 LANDSAT Mosaic (processed very well by TerraMetrics) with Digital Globe and other higher resolution imagery (largely post-2000 in origin) added for large parts of Europe and the US and for cities and other areas of interest across much of the rest of the world. We have added the public domain LANDSAT MSS Mosaic (circa 1975) and the LANDSAT TM Mosaic (1990) for most of the world. We are also running the LANDSAT 2000 Mosaic and adding that for parts of the world so that - as Google update their imagery - the following history will be visible : (all circa) 1975, 1990, 2000 and Google native (post 2000). The aim is to help visualise land cover change in a simple way. The images have been converted to 'natural' color. Invariably the data quality and resolution are reduced as we go back in time and some parts of the globe are missing for various reasons, including the degradation of data storage media at the LANDSAT stations, but nevertheless a useful simple visiualisation is possible. Click the link and when Google Earth opens up zoom in to the area of your interest (best to start within the tropics). The 1990 layer will start to load once you get close enough. Zoom in for full resolution. Use the slider to compare with the Google native imagery underneath.

There are also layers for coverage of the different datasets and metadata (image names and dates of capture for the individual LANDSAT images) for the 1990 layer. Activate the 1990 metadata layer and zoom in for the placemarks if you want to see the metadata. This is very much a work in progress.

If you see any interesting land cover history then use the built-in GEOWIKI to tell others about it.

Source: KCL, Mark Mulligan

This research aimed to assess the observed land use and land cover (LULC) changes of Bale Mountains National Park (BMNP) from 1993 to 2023 and its future projections for the years (2033 and 2053). The study utilized multi-date Landsat imagery from 1993, 2003, 2013, and 2023, leveraging Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI-TIRS sensors for LULC classification. Standard image pre-processing techniques were applied, and composite images were created using yearly median values in Google Earth Engine (GEE). In addition to satellite data, both physical and socioeconomic variables were used as input for future LULC modeling. The Random Forest (RF) classification algorithm was used for image classification, while the Cellular Automata Artificial Neural Networks (CA-ANN) model within the Modules for Land Use Change Simulations (MOLUSCE) plugin of QGIS was employed for future LULC projection. The analysis revealed significant LULC changes in BMNP, from 1993 to 2023, primarily due to anthropogenic activities, with further changes anticipated between 2023 and 2053.The results showed a notable increase in woodland and shrubs at the expense of grassland and Erica forest. While woodland and shrubs increased by 87.18% and 36.7%, areas of Erica forest and grassland lost about 25% and 22% of their area, respectively, during this period. The LULC model results also indicated that areas covered by woodland and shrubs are expected to increase by 15.97% and 15.57%, respectively, between 2023 and 2053. Conversely, land areas occupied by cultivated land, Erica forest, grassland, and herbaceous plants are projected to decrease by 28.52%, 3.28%, 19.03%, and 6.55%, respectively. Proximity to roads and urban areas combined with rising temperatures and altered precipitation patterns emerged as critical factors influencing land use conversion patterns in BMNP. These findings underscore the complex interplay between environmental factors and human activities in shaping land cover dynamics. Hence, promoting sustainable land management practices among the park administration and local community as well as enhancing habitat protection efforts are recommended. Additionally, integrating advanced remote sensing technologies with ground truthing efforts will be essential for accurate assessments of LULC dynamics in this critical area of biodiversity.

The repository contains observed Above Ground Biomass (AGB) estimates at about 275 sample plots chosen for AGB assessment in the forests of Kashmir Himalaya. The AGB is assessed as a fucntion of dbh using various allometric equations developed specifically for the region. It also contains the AGB for years 1978, 1990, 2000, 2010 and 2021 predicted using topographcally augmeneted multivariate regression model. The extent of forest, delineated using on-screen digitization using Landsat and Sentinel image collection at decadal scale is also provided for the years 1978, 1990, 2000, 2010 and 2021.

The linked change detection datasets include change detection model runs generated using LandTrendr, Landsat-based Detection of Trends in Disturbance and Recovery, which was developed by the Oregon State University Environmental Monitoring, Analysis, and Process Recognition Lab (Kennedy, R.E., 2018), and CCDC-SMA, Continuous Change Detection and Classification - Spectral Mixture Analysis, which was developed by Dr. Shijuan Chen, (Chen, S., 2021). These runs were parameterized individually for the following time periods: 2000-2024 for algorithm tuning, 2018-2024 for change output analysis, and 2024 for mangrove validation, and were tuned within their associated Google Earth Engine Application Programming Interface.

The linked extent file is the 2024 stable mangrove extent generated by applying the best performing change detection model run to the Cherrington et al., 2020 stable mangrove extent from 2017 (linked here: doi: 10.17632/2fzbnpnsdp.1)

A full description of the process by which the data were generated is provided in “Cloud-based solutions for monitoring coastal ecosystems and prioritization of restoration efforts across Belize.”